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11 pages, 2391 KiB

Open AccessArticle

Guiding Breathing at the Resonance Frequency with Haptic Sensors Potentiates Cardiac Coherence

by Pierre Bouny, Laurent M. Arsac, Antoine Guérin, Guillam Nerincx and Veronique Deschodt-Arsac

Sensors 2023, 23(9), 4494; https://doi.org/10.3390/s23094494 - 05 May 2023

Cited by 1 | Viewed by 2502

Abstract

Cardiac coherence is a state achieved when one controls their breathing rate during the so-called resonance frequency breathing. This maneuver allows respiratory-driven vagal modulations of the heart rate to superimpose with sympathetic modulations occurring at 0.1 Hz, thereby maximizing autonomous power in heart-to-brain [...] Read more.

Cardiac coherence is a state achieved when one controls their breathing rate during the so-called resonance frequency breathing. This maneuver allows respiratory-driven vagal modulations of the heart rate to superimpose with sympathetic modulations occurring at 0.1 Hz, thereby maximizing autonomous power in heart-to-brain connections. These stimulations have been shown to improve vagal regulations, which results in obvious benefits for both mental and organic health. Here, we present a device that is able to deliver visual and haptic cues, as well as HRV biofeedback information to guide the user in maintaining a 0.1 Hz breathing frequency. We explored the effectiveness of cardiac coherence in three guidance conditions: visual, haptic and visuo-haptic breathing. Thirty-two healthy students (sixteen males) were divided into three groups that experienced five minutes of either visual, haptic and visuo-haptic guided breathing at 0.1 Hz. The effects of guidance on the (adequate) breathing pattern and heart rate variability (HRV) were analyzed. The interest of introducing haptic breathing to achieve cardiac coherence was shown in the haptic and visuo-haptic groups. Especially, the P_0.1 index, which indicates how the autonomous power is ‘concentrated’ at 0.1 Hz in the PSD spectrum, demonstrated the superiority of combining haptic with visual sensory inputs in potentiating cardiac coherence (0.55 ± 0.20 for visuo-haptic vs. 0.28 ± 0.14 for visual only guidance; p < 0.05) haptic-induced effectiveness could be an asset for a more efficient and time-saving practice, allowing improved health and well-being even under tight time constraints. Full article

(This article belongs to the Special Issue Advanced Sensors and Applications for Heart Rate and Heart Rate Variability)

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17 pages, 1053 KiB

Open AccessArticle

The Validity of Ultra-Short-Term Heart Rate Variability during Cycling Exercise

by Yukiya Tanoue, Shihoko Nakashima, Tomohiro Komatsu, Miki Kosugi, Saki Kawakami, Shotaro Kawakami, Ryoma Michishita, Yasuki Higaki and Yoshinari Uehara

Sensors 2023, 23(6), 3325; https://doi.org/10.3390/s23063325 - 22 Mar 2023

Cited by 1 | Viewed by 2063

Abstract

Ultra-short-term heart rate variability (HRV) has been validated in the resting state, but its validity during exercise is unclear. This study aimed to examine the validity in ultra-short-term HRV during exercise considering the different exercise intensities. HRVs of twenty-nine healthy adults were measured [...] Read more.

Ultra-short-term heart rate variability (HRV) has been validated in the resting state, but its validity during exercise is unclear. This study aimed to examine the validity in ultra-short-term HRV during exercise considering the different exercise intensities. HRVs of twenty-nine healthy adults were measured during incremental cycle exercise tests. HRV parameters (Time-, frequency-domain and non-linear) corresponding to each of the 20% (low), 50% (moderate), and 80% (high) peak oxygen uptakes were compared between the different time segments of HRV analysis (180 s (sec) segment vs. 30, 60, 90, and 120-sec segments). Overall, the differences (bias) between ultra-short-term HRVs increased as the time segment became shorter. In moderate- and high-intensity exercises, the differences in ultra-short-term HRV were more significant than in low intensity exercise. Thus, we discovered that the validity of ultra-short-term HRV differed with the duration of the time segment and exercise intensities. However, the ultra-short-term HRV is feasible in the cycling exercise, and we determined some optimal time duration for HRV analysis for across exercise intensities during the incremental cycling exercise. Full article

(This article belongs to the Special Issue Advanced Sensors and Applications for Heart Rate and Heart Rate Variability)

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17 pages, 1449 KiB

Open AccessArticle

Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases

by Szymon Sieciński, Ewaryst Janusz Tkacz and Paweł Stanisław Kostka

Sensors 2023, 23(4), 2152; https://doi.org/10.3390/s23042152 - 14 Feb 2023

Cited by 6 | Viewed by 2486

Abstract

Heart rate variability (HRV) is the physiological variation in the intervals between consecutive heartbeats that reflects the activity of the autonomic nervous system. This parameter is traditionally evaluated based on electrocardiograms (ECG signals). Seismocardiography (SCG) and/or gyrocardiography (GCG) are used to monitor cardiac [...] Read more.

Heart rate variability (HRV) is the physiological variation in the intervals between consecutive heartbeats that reflects the activity of the autonomic nervous system. This parameter is traditionally evaluated based on electrocardiograms (ECG signals). Seismocardiography (SCG) and/or gyrocardiography (GCG) are used to monitor cardiac mechanical activity; therefore, they may be used in HRV analysis and the evaluation of valvular heart diseases (VHDs) simultaneously. The purpose of this study was to compare the time domain, frequency domain and nonlinear HRV indices obtained from electrocardiograms, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) in healthy volunteers and patients with valvular heart diseases. An analysis of the time domain, frequency domain and nonlinear heart rate variability was conducted on electrocardiograms and gyrocardiograms registered from 29 healthy male volunteers and 30 patients with valvular heart diseases admitted to the Columbia University Medical Center (New York City, NY, USA). The results of the HRV analysis show a strong linear correlation with the HRV indices calculated from the ECG, SCG and GCG signals and prove the feasibility and reliability of HRV analysis despite the influence of VHDs on the SCG and GCG waveforms. Full article

(This article belongs to the Special Issue Advanced Sensors and Applications for Heart Rate and Heart Rate Variability)

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22 pages, 4081 KiB

Open AccessArticle

Dual Wavelength Photoplethysmography Framework for Heart Rate Calculation

by Ludvik Alkhoury, JiWon Choi, Vishnu D. Chandran, Gabriela B. De Carvalho, Saikat Pal and Moshe Kam

Sensors 2022, 22(24), 9955; https://doi.org/10.3390/s22249955 - 17 Dec 2022

Cited by 1 | Viewed by 1887

Abstract

The quality of heart rate (HR) measurements extracted from human photoplethysmography (PPG) signals are known to deteriorate under appreciable human motion. Auxiliary signals, such as accelerometer readings, are usually employed to detect and suppress motion artifacts. A 2019 study by Yifan Zhang and [...] Read more.

The quality of heart rate (HR) measurements extracted from human photoplethysmography (PPG) signals are known to deteriorate under appreciable human motion. Auxiliary signals, such as accelerometer readings, are usually employed to detect and suppress motion artifacts. A 2019 study by Yifan Zhang and his coinvestigatorsused the noise components extracted from an infrared PPG signal to denoise a green PPG signal from which HR was extracted. Until now, this approach was only tested on “micro-motion” such as finger tapping. In this study, we extend this technique to allow accurate calculation of HR under high-intensity full-body repetitive “macro-motion”. Our Dual Wavelength (DWL) framework was tested on PPG data collected from 14 human participants while running on a treadmill. The DWL method showed the following attributes: (1) it performed well under high-intensity full-body repetitive “macro-motion”, exhibiting high accuracy in the presence of motion artifacts (as compared to the leading accelerometer-dependent HR calculation techniques TROIKA and JOSS); (2) it used only PPG signals; auxiliary signals such as accelerometer signals were not needed; and (3) it was computationally efficient, hence implementable in wearable devices. DWL yielded a Mean Absolute Error

(M A E)

of 1.22|0.57 BPM, Mean Absolute Error Percentage

(M A E P)

of 0.95|0.38%, and performance index

(P I)

(which is the frequency, in percent, of obtaining an HR estimate that is within ±5 BPM of the HR ground truth) of 95.88|4.9%. Moreover, DWL yielded a short computation period of 3.0|0.3 s to process a 360-second-long run. Full article

(This article belongs to the Special Issue Advanced Sensors and Applications for Heart Rate and Heart Rate Variability)

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15 pages, 1732 KiB

Open AccessArticle

A Novel Adaptive Noise Elimination Algorithm in Long RR Interval Sequences for Heart Rate Variability Analysis

by Vytautas Stankus, Petras Navickas, Anžela Slušnienė, Ieva Laucevičienė, Albinas Stankus and Aleksandras Laucevičius

Sensors 2022, 22(23), 9213; https://doi.org/10.3390/s22239213 - 26 Nov 2022

Cited by 1 | Viewed by 1181

Abstract

As heart rate variability (HRV) studies become more and more prevalent in clinical practice, one of the most common and significant causes of errors is associated with distorted RR interval (RRI) data acquisition. The nature of such artifacts can be both mechanical as [...] Read more.

As heart rate variability (HRV) studies become more and more prevalent in clinical practice, one of the most common and significant causes of errors is associated with distorted RR interval (RRI) data acquisition. The nature of such artifacts can be both mechanical as well as software based. Various currently used noise elimination in RRI sequences methods use filtering algorithms that eliminate artifacts without taking into account the fact that the whole RRI sequence time cannot be shortened or lengthened. Keeping that in mind, we aimed to develop an artifacts elimination algorithm suited to long-term (hours or days) sequences that does not affect the overall structure of the RRI sequence and does not alter the duration of data registration. An original adaptive smart time series step-by-step analysis and statistical verification methods were used. The adaptive algorithm was designed to maximize the reconstruction of the heart-rate structure and is suitable for use, especially in polygraphy. The authors submit the scheme and program for use. Full article

(This article belongs to the Special Issue Advanced Sensors and Applications for Heart Rate and Heart Rate Variability)

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14 pages, 2085 KiB

Open AccessArticle

Validity of Ultra-Short-Term HRV Analysis Using PPG—A Preliminary Study

by Aline Taoum, Alexis Bisiaux, Florian Tilquin, Yann Le Guillou and Guy Carrault

Sensors 2022, 22(20), 7995; https://doi.org/10.3390/s22207995 - 20 Oct 2022

Cited by 6 | Viewed by 2170

Abstract

Continuous measurement of heart rate variability (HRV) in the short and ultra-short-term using wearable devices allows monitoring of physiological status and prevention of diseases. This study aims to evaluate the agreement of HRV features between a commercial device (Bora Band, Biosency) measuring photoplethysmography [...] Read more.

Continuous measurement of heart rate variability (HRV) in the short and ultra-short-term using wearable devices allows monitoring of physiological status and prevention of diseases. This study aims to evaluate the agreement of HRV features between a commercial device (Bora Band, Biosency) measuring photoplethysmography (PPG) and reference electrocardiography (ECG) and to assess the validity of ultra-short-term HRV as a surrogate for short-term HRV features. PPG and ECG recordings were acquired from 5 healthy subjects over 18 nights in total. HRV features include time-domain, frequency-domain, nonlinear, and visibility graph features and are extracted from 5 min 30 s and 1 min 30 s duration PPG recordings. The extracted features are compared with reference features of 5 min 30 s duration ECG recordings using repeated-measures correlation, Bland–Altman plots with 95% limits of agreements, Cliff’s delta, and an equivalence test. Results showed agreement between PPG recordings and ECG reference recordings for 37 out of 48 HRV features in short-term durations. Sixteen of the forty-eight HRV features were valid and retained very strong correlations, negligible to small bias, with statistical equivalence in the ultra-short recordings (1 min 30 s). The current study concludes that the Bora Band provides valid and reliable measurement of HRV features in short and ultra-short duration recordings. Full article

(This article belongs to the Special Issue Advanced Sensors and Applications for Heart Rate and Heart Rate Variability)

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21 pages, 1211 KiB

Open AccessArticle

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

by Adam G. Polak, Bartłomiej Klich, Stanisław Saganowski, Monika A. Prucnal and Przemysław Kazienko

Sensors 2022, 22(18), 7047; https://doi.org/10.3390/s22187047 - 17 Sep 2022

Cited by 5 | Viewed by 2011

Abstract

Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts pulse rate variability (PRV). This study aims to [...] Read more.

Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts pulse rate variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to heart rate variability (HRV) derived from the accompanying ECG. The results indicate that filtering PPG signals using the discrete wavelet transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Post-processing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the autoregressive (AR) model is only important when the PPG is of low quality and has no effect under good signal quality. The main conclusion is that the DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms, particularly at rest. However, post-processing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments. Full article

(This article belongs to the Special Issue Advanced Sensors and Applications for Heart Rate and Heart Rate Variability)

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8 pages, 234 KiB

Open AccessStudy Protocol

The Usefulness of Assessing Heart Rate Variability in Patients with Acute Myocardial Infarction (HeaRt-V-AMI)

by Crischentian Brinza, Mariana Floria, Adrian Covic, Andreea Covic, Dragos-Viorel Scripcariu and Alexandru Burlacu

Sensors 2022, 22(9), 3571; https://doi.org/10.3390/s22093571 - 07 May 2022

Cited by 4 | Viewed by 1922

Abstract

Background: Heart rate variability (HRV) could have independent and critical prognostic values in patients admitted for ST segment elevation myocardial infarction (STEMI). There are limited data in the literature regarding HRV assessment in STEMI setting. Thus, we aim to investigate the potential correlations [...] Read more.

Background: Heart rate variability (HRV) could have independent and critical prognostic values in patients admitted for ST segment elevation myocardial infarction (STEMI). There are limited data in the literature regarding HRV assessment in STEMI setting. Thus, we aim to investigate the potential correlations between HRV and adverse outcomes in a contemporary cohort of patients presenting with STEMI undergoing primary percutaneous coronary intervention (PCI). Methods: We will perform a prospective, observational cohort study in a single healthcare center. Adult patients aged ≥18 years presenting with STEMI in sinus rhythm will be enrolled for primary PCI within 12 h from symptoms onset. Time domain, frequency domain, and nonlinear HRV parameters will be measured using a medically approved wrist-wearable device for 5 min segments during myocardial revascularization by primary PCI. Additional HRV measurements will be performed one and six months from the index event. The primary composite outcome will include all-cause mortality and major adverse cardiovascular events (during the hospital stay, one month, and one year following admission). Several secondary outcomes will be analyzed: individual components of the primary composite outcome, target lesion revascularization, hospitalizations for heart failure, ventricular arrhythmias, left ventricular ejection fraction, and left ventricular diastolic function. Conclusions: Our study will enlighten the reliability and usefulness of HRV evaluation as a prognostic marker in contemporary patients with STEMI. The potential validation of HRV as a risk marker for adverse outcomes following STEMI will ensure a background for including HRV parameters in future risk scores and guidelines. Full article

(This article belongs to the Special Issue Advanced Sensors and Applications for Heart Rate and Heart Rate Variability)

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